Cleansing system for a feed composition based on environmental factors

ABSTRACT

A cleansing system for improving operation of a petrochemical plant or refinery. The petrochemical plant or refinery may include a fractionation column, a condenser, and a pump. Equipment, such as condensers, receivers, reboilers, feed exchangerss, and pumps may be divided into subsections. Temperatures, pressures, flows, and other plant operations may be used for optimizing plant performance. A cleansing unit performs an enhanced cleansing process, which may allow early detection and diagnosis of the plant operating conditions based on one or more environmental factors.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation in part of U.S. application Ser. No.15/084,319, filed Mar. 29, 2016, which claims priority under 35 U.S.C.§119(e) of U.S. Provisional Application No. 62/140,043, filed Mar. 30,2015, each of which is incorporated herein by reference in its entirety.

FIELD

The present disclosure is related to a cleansing processes for a plant,such as a chemical plant or refinery, and more particularly a cleansingprocess for inferring a feed composition.

BACKGROUND

Companies operating refineries and petrochemical plants typically facetough challenges in today's environment. These challenges may includeincreasingly complex technologies, a reduction in workforce experiencelevels, and constantly changing environmental regulations.

Furthermore, as feed and product supplies become more volatile,operators often find it more difficult to make operating decisions thatmay optimize their approach. This volatility may be unlikely to ease inthe foreseeable future, but may represent potential to those companiesthat can quickly identify and respond to opportunities as they arise.

Outside pressures generally force operating companies to continuallyincrease the usefulness of existing assets. In response, catalyst,adsorbent, equipment, and control system suppliers develop more complexsystems that can increase performance. Maintenance and operations ofthese advanced systems generally requires increased skill levels thatmay be difficult to develop, maintain, and transfer, given the timepressures and limited resources of today's technical personnel. Theseincreasingly complex systems are not always operated to their highestpotential. In addition, when existing assets are operated close to andbeyond their design limits, reliability concerns and operational risksmay increase.

Plant operators typically respond to the above challenges with one ormore strategies, such as, for example, availability risk reduction,working the value chain, and continuous optimization. Availability riskreduction emphasizes achieving adequate plant operations as opposed tomaximizing performance. Working the value chain emphasizes improving thematch of feed and product mix with asset capabilities and outsidedemands. Continuous optimization employs tools, systems, and models tocontinuously monitor and bridge gaps in plant performance.

In a typical data cleansing process, only flow meters are corrected.Data cleansing is performed to correct flow meter calibration and fluiddensity changes, after which the total error of flow meters in a massbalance envelope is averaged to force a 100% mass balance between thenet feed and net product flows. But this conventional data cleansingpractice ignores other related process information available (e.g.,temperatures, pressures, and internal flows) and does not allow for anearly detection of a significant error. Specifically, the errorsassociated with the flow meters are distributed among the flow meters,and thus it is difficult to detect an error of a specific flow meter.

Typically, plant measurements including sensor data are collected on acontinual basis, while laboratory measurements are intermittentlysampled and delivered to a laboratory for analysis. Thus, whenevaluating plant performance based on the actual operating data, it isoften difficult to determine a state of health of the plant operationdue to a time lag in receiving plant laboratory data.

In many cases, because the laboratory data is collected at a sparse timeinterval, such as once a day or week, the laboratory data is unavailableduring the interval, and thus becomes outdated. Due to the time toupdated laboratory data, the plant operators often use the most recentlyavailable set of laboratory data for performance evaluation, and assumethat the last laboratory data set is still appropriate for the currentoperating data. This assumption is frequently misleading andinappropriate because the last laboratory data set may be unreliable atthe time of plant performance evaluation.

Therefore, there is a need for an improved data cleansing system andmethod that performs an early detection and diagnosis of plant operationusing environmental factors without substantially relying on laboratorydata.

SUMMARY

A general object of the disclosure is to improve operation efficiency ofchemical plants and refineries. A more specific object of thisdisclosure is to overcome one or more of the problems described above. Ageneral object of this disclosure may be attained, at least in part,through a method for improving operation of a plant. The method includesobtaining plant operation information from the plant.

The present disclosure further comprehends a method for improvingoperation of a plant that includes obtaining plant operation informationfrom the plant and generating a plant process model using the plantoperation information. This disclosure still further comprehends amethod for improving operation of a plant. The method includes receivingplant operation information over a network, such as the internet, andautomatically generating a plant process model using the plant operationinformation.

An enhanced data cleansing process may allow an early detection anddiagnosis of measurement errors based on one or more environmentalfactors. The environmental factors may include at least one primaryfactor. The primary factor may include, for example, a temperature, apressure, a feed flow, a product flow, and/or the like. Theenvironmental factors may include a secondary factor. The secondaryfactor may include, for example, a density, a specific composition,and/or the like. Using the primary and secondary factors, at least oneoffset between the measurement and the process model information may becalculated. The offsets may be used to infer the feed composition thatcorresponds with available plant operation data.

The present disclosure may use configured process models to reconcilemeasurements within individual process units, operating blocks, and/orcomplete processing systems. Routine and frequent analysis of modelpredicted values versus actual measured values may allow earlyidentification of measurement errors, which may be acted upon tominimize impact on operations.

The present disclosure may use process measurements from any of thefollowing devices: pressure sensors, differential pressure sensors,orifice plates, venturi, other flow sensors, temperature sensors,capacitance sensors, weight sensors, gas chromatographs, moisturesensors, and/or other sensors commonly found in the refining andpetrochemical industry. Further, the present disclosure may use processlaboratory measurements from gas chromatographs, liquid chromatographs,distillation measurements, octane measurements, and/or other laboratorymeasurements commonly found in the refining and petrochemical industry.

The process measurements may be used to monitor the performance of anyof the following process equipment: pumps, compressors, heat exchangers,fired heaters, control valves, fractionation columns, reactors, and/orother process equipment commonly found in the refining and petrochemicalindustry.

The method of this disclosure may be implemented using a local orweb-based computer system. The benefits of executing work processeswithin this platform may include improved plant performance due to anincreased ability by operations to identify and/or captureopportunities, a sustained ability to bridge performance gaps, anincreased ability to leverage personnel expertise, and improvedenterprise tuning.

A data collection system at a plant may capture data and automaticallysend captured data to a remote location, where the data may be reviewedto, for example, eliminate errors and biases, and/or used to calculateand report performance results. The performance of the plant and/orindividual process units of the plant may be compared to the performancepredicted by one or more process models to identify any operatingdifferences or gaps.

A report (e.g., an hourly report, a daily report, a weekly report, amonthly report) showing actual measured values compared to predictedvalues may be generated and delivered to a plant operator and/or a plantor third party process engineer over a network (e.g., the internet).Identifying performance gaps may allow the operators and/or engineers toidentify and resolve the cause of the gaps. In some embodiments, themethod may include using the process models and plant operationinformation to run optimization routines that converge on an optimalplant operation (e.g., for given values of feed, products, and/orprices).

The method of this disclosure may provide plant operators and/orengineers with regular advice that may enable recommendations to adjustsetpoints or reference points allowing the plant to run continuously ator closer to optimal conditions. The method of this disclosure mayprovide the operator alternatives for improving or modifying the futureoperations of the plant. The method of this disclosure may regularlymaintain and/or tune the process models to correctly represent the truepotential performance of the plant. The method may include optimizationroutines configured per specific criteria, which are used to identifyoptimum operating points, evaluate alternative operations, and/orevaluate feed.

The present disclosure may provide a repeatable method that may helprefiners bridge the gap between actual and achievable performance. Themethod of this disclosure may use process development history, modelingcharacterization, stream characterization, and/or plant automationexperience to address the issues of ensuring data security as well asefficient aggregation, tuning, and movement of large amounts of data.Web-based optimization may enable achieving and sustaining maximumprocess performance by connecting, on a virtual basis, technicalexpertise and the plant process operations staff.

The enhanced workflow may use configured process models to monitor,predict, and/or optimize performance of individual process units,operating blocks, or complete processing systems. Routine and frequentanalysis of predicted versus actual performance may allow earlyidentification of operational discrepancies that may be acted upon tooptimize impact.

As used herein, references to a “routine” refer to a sequence ofcomputer programs or instructions for performing a particular task.References herein to a “plant” refer to any of various types of chemicaland petrochemical manufacturing or refining facilities. Referencesherein to a plant “operators” refer to and/or include, withoutlimitation, plant planners, managers, engineers, technicians, and othersinterested in, overseeing, and/or running the daily operations at aplant.

In some embodiments, a cleansing system may be provided for improvingmeasurement error estimation and detection. A server may be coupled tothe cleansing system for communicating with the plant via acommunication network. A computer system may include a web-basedplatform for receiving and sending plant data related to the operationof the plant over the network. A display device may interactivelydisplay the plant data. A data cleansing unit may be configured forperforming an enhanced data cleansing process for allowing an earlydetection and diagnosis of the measurement errors of the plant based onat least one environmental factor. A feed estimation unit may beconfigured for estimating a feed composition associated with the plantbased on the calculated offset amount between the measured and simulatedvalues. The feed estimation unit may evaluate the calculated offsetamount based on the at least one environmental factor.

In some embodiments, a cleansing method for improving measurement errordetection of a plant may include providing a server coupled to acleansing system for communicating with the plant via a communicationnetwork; providing a computer system having a web-based platform forreceiving and sending plant data related to the operation of the plantover the network; providing a display device for interactivelydisplaying the plant data, the display device being configured forgraphically or textually receiving the plant data; obtaining the plantdata from the plant over the network; performing an enhanced datacleansing process for allowing an early detection and diagnosis of themeasurement errors of the plant based on at least one environmentalfactor; calculating and evaluating an offset amount representing adifference between measured and simulated values ; estimating a feedcomposition associated with the plant based on the calculated offsetamount between the feed and product information; and/or evaluating thecalculated offset amount based on the at least one environmental factorfor detecting the error of the equipment during the operation of theplant.

The foregoing and other aspects and features of the present disclosurewill become apparent to those of reasonable skill in the art from thefollowing detailed description, as considered in conjunction with theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an illustrative use of a data cleansing system in anetwork infrastructure in accordance with one or more embodiments of thepresent disclosure;

FIG. 2 depicts an illustrative functional block diagram of a datacleansing system featuring functional units in accordance with one ormore embodiments of the present disclosure;

FIG. 3 depicts an illustrative functional block diagram of a datacleansing system featuring an illustrative arrangement of a datacleansing unit and a feed estimation unit in accordance with one or moreembodiments of the present disclosure; and

FIG. 4 depicts an illustrative flowchart of a data cleansing method inaccordance with one or more embodiments of the present disclosure.

DETAILED DESCRIPTION

Referring now to FIG. 1, an illustrative data cleansing system,generally designated 10, using an embodiment of the present disclosuremay be provided for improving operation of one or more plants (e.g.,Plant A . . . Plant N) 12 a-12 n, such as a chemical plant or refinery,or a portion thereof. The present data cleansing system 10 may use plantoperation information obtained from one or more plants 12 a-12 n.

As used herein, the term “system,” “unit” or “module” may refer to, bepart of, or include an Application Specific Integrated Circuit (ASIC),an electronic circuit, non-transitory memory (shared, dedicated, orgroup) and/or a computer processor (shared, dedicated, or group) thatexecutes one or more computer-readable instructions (e.g., software orfirmware programs), a combinational logic circuit, and/or other suitablecomponents that provide the described functionality. Non-transitorymedia may be provided that store computer-readable instructions thatimplement one or more aspects of the disclosure. Thus, while thisdisclosure includes particular examples and arrangements of the units,the scope of the present system is not so limited, since othermodifications will become apparent to the skilled practitioner.

The data cleansing system 10 may reside in or be coupled to a server orcomputing device 14 (including, e.g., database and video servers), andmay be programmed to perform tasks and display relevant data fordifferent functional units via a communication network 16, in someembodiments using a secured cloud computing infrastructure. Othersuitable networks may be used, such as the internet, a wireless network(e.g., Wi-Fi), a corporate Intranet, a local area network (LAN), and/ora wide area network (WAN), and the like, using dial-in connections,cable modems, high-speed ISDN lines, and/or other types of communicationmethods. Relevant information may be stored in databases for retrievalby the data cleansing system 10 or the computing device 14 (e.g., as adata storage device and/or one or more machine-readable data-storagemedia storing computer programs).

The data cleansing system 10 may be partially or fully automated. Insome embodiments, the data cleansing system 10 may be performed by acomputer system, such as a third-party computer system, local to orremote from the one or more plants 12 a-12 n and/or the plant planningcenter. The data cleansing system 10 may include a web-based platform 18that obtains or receives and sends information over a network (e.g., theinternet). Specifically, the data cleansing system 10 may receivesignals and/or parameters from one or more plants 12 a-12 n via thecommunication network 16, and may display (e.g., in real time or with ashort delay) related performance information on an interactive displaydevice 20 accessible to an operator or user.

Using a web-based system for implementing the method of this disclosuremay provide benefits, such as improved plant performance due to anincreased ability by plant operators to identify and captureopportunities, a sustained ability to bridge plant performance gaps,and/or an increased ability to leverage personnel expertise and improvetraining and development. The method of this disclosure may allow forautomated daily evaluation of process measurements, thereby increasingthe frequency of performance review with less time and effort from plantoperations staff.

The web-based platform 18 may allow multiple users to work with the sameinformation, thereby creating a collaborative environment for sharingbest practices or for troubleshooting. The method of this disclosure mayprovide more accurate prediction and optimization results due to fullyconfigured models that may include, for example, catalytic yieldrepresentations, constraints, degrees of freedom, and the like. Routineautomated evaluation of plant planning and operation models may allowtimely plant model tuning to reduce or eliminate gaps between plantmodels and the actual plant performance. Implementing the method of thisdisclosure using the web-based platform 18 may also allow for monitoringand/or updating multiple sites, thereby better enabling facilityplanners to propose realistic optimal targets.

Referring now to FIG. 2, the present data cleansing system 10 mayinclude a reconciliation unit 22 configured for reconciling actualmeasured data from the respective one or more plants 12 a-12 n withprocess model results from a simulation engine. The reconciling may bebased on a set of reference or set points. In some embodiments, aheuristic analysis may be performed against the actual measured data andthe process model results using a set of predetermined threshold values.In some embodiments, the threshold values may be determined from pastplant performance. A statistical analysis and/or other suitable analytictechniques may be used to suit different applications.

As an example, plant operating parameters, such as temperatures,pressures, feed compositions, fractionation column product compositions,and/or the like, may be received from the respective one or more plants12 a-12 n. These plant parameters may represent the actual measured datafrom selected pieces of equipment in the one or more plants 12 a-12 nduring a predetermined time period. The reconciliation unit 22 maycompare these plant operational parameters with the process modelresults from the simulation engine based on the predetermined thresholdvalues.

The data cleansing system 10 may include an interface module 24 forproviding an interface between the data cleansing system 10, one or moreinternal or external databases 26, and/or the network 16. The interfacemodule 24 may receive data from, for example, plant sensors via thenetwork 16, and/or other related system devices, services, andapplications. The other devices, services, and applications may include,but are not limited to, one or more software or hardware componentsrelated to the respective one or more plants 12 a-12 n. The interfacemodule 24 may also receive the signals and/or parameters, which may becommunicated to the respective units and modules, such as the datacleansing system 10, and its associated computing modules or units.

By performing data reconciliation over an entire sub-section of theflowsheet, some or all of the process data relating to particularequipment may be used to reconcile the associated operational plantparameters. As described in greater detail below, in some embodiments,at least one plant operational parameter, such as a mass flow rate, maybe utilized in the correction of the mass balance. Offsets calculatedfor the plant measurements may be tracked and stored in the database 26for subsequent retrieval.

A data cleansing unit 28 may be provided for performing an enhanced datacleansing process for allowing an early detection and diagnosis of plantoperation based on one or more environmental factors. As discussedabove, the environmental factors may include at least one primaryfactor. The primary factor may include, for example, a temperature, apressure, a feed flow, a product flow, and/or the like. Theenvironmental factors may include one or more secondary factors. Thesecondary factor may include, for example, a density, a specificcomposition, and/or the like. An offset amount representing a differencebetween the feed and product information may be calculated and/orevaluated for detecting an error of specific equipment during plantoperation.

In operation, the data cleansing unit 28 may receive at least one set ofactual measured data from a customer site or one or more plants 12 a-12n on a recurring basis at a specified time interval (e.g., every 100milliseconds, every second, every ten seconds, every minute, every twominutes, every five minutes, every ten minutes, every thirty minutes,every hour). For data cleansing, the received data may be analyzed forcompleteness and/or corrected for gross errors by the data cleansingunit 28. Then, the data may be corrected for measurement issues (e.g.,an accuracy problem for establishing a simulation steady state) andoverall mass balance closure to generate a duplicate set of reconciledplant data.

The data cleansing system 10 may include a prediction unit 34 configuredto use the corrected data as an input to a simulation process, in whichthe process model is tuned to ensure that the simulation process matchesthe reconciled plant data. The prediction unit 34 may perform such thatan output of the reconciled plant data is inputted into a tunedflowsheet, and then is generated as a predicted data. Each flowsheet maybe a collection of virtual process model objects as a unit of processdesign. A delta value, which is a difference between the reconciled dataand the predicted data, may be validated so that a viable optimizationcase may be established for a simulation process run.

The data cleansing system 10 may include an optimization unit 36configured to use the tuned simulation engine as a basis for theoptimization case, which may be run with a set of the reconciled data asan input. The output from this step may be a new set of optimized data.A difference between the reconciled data and the optimized data mayprovide an indication as to how the operations may be changed to reach agreater optimum. In this configuration, the data cleansing unit 28 mayprovide a user-configurable method for minimizing objective functions,thereby maximizing efficiency of the one or more plants 12 a-12 n.

A feed estimation unit 30 may be provided for estimating the feedcomposition associated with specific plant equipment based on thecalculated offset amount between the feed (or input) and product (oroutput) information. Initially, the feed estimation unit 30 may evaluatethe calculated offsets between the measured and simulated flow based onthe at least one environmental factor for detecting a measurement errorduring plant operation. As described in greater detail below, a lastknown reliable feed composition may be established as a base point, andthe last known feed composition may be modified to provide more accuratecomposition data based on the calculated offsets.

The present data cleansing system 10 may include a diagnosis unit 32configured for diagnosing an operational status of a measurement basedon at least one environmental factor. In some embodiments, the diagnosisunit 32 may receive the plant measurements and process simulation fromthe one or more plants 12 a-12 n to proactively evaluate a specificpiece of plant equipment. To evaluate various limits of a particularprocess and stay within the acceptable range of limits, the diagnosisunit 32 may determine target tolerance levels of a final product basedon actual current and/or historical operational parameters, for example,from a flow rate, a heater, a temperature set point, a pressure signal,and/or the like.

The diagnosis unit 32 may receive the calculated offsets from the feedestimation unit 30 for evaluation. When the offsets are different frompreviously calculated offsets by a predetermined value, the diagnosisunit 32 may determine that the specific measurement is faulty or inerror. In some embodiments, an additional reliability heuristic analysismay be performed on this diagnosis.

In using the kinetic model or other detailed calculations, the diagnosisunit 32 may establish boundaries or thresholds of operating parametersbased on existing limits and/or operating conditions. Illustrativeexisting limits may include mechanical pressures, temperature limits,hydraulic pressure limits, and/or operating lives of various components.Other suitable limits and conditions are contemplated to suit differentapplications.

Referring now to FIG. 3, an illustrative arrangement of the datacleansing unit 28 and the feed estimation unit 30 is illustrated inaccordance with one or more embodiments of the present data cleansingsystem 10. In some embodiments, the data cleansing unit 28 may receiveprocess model information relating to the current process model of thesimulation engine, current plant process data associated with thespecific plant equipment, and/or current plant laboratory dataassociated with the specific plant equipment. The offsets calculatedbased on the feed and product information may be transmitted to the feedestimation unit 30 for evaluation. In some embodiments, plantperformance fit parameters may be transmitted to the feed estimationunit 30.

After the data cleansing unit 28 tunes the process model, the datacleansing unit 28 may determine a state of health of the process modelbased on the tuning results. For example, the state of health of theprocess model may be determined based on an error margin measuredbetween the actual measured data and the calculated performance processmodel results. When the error margin is greater than a predeterminedthreshold, an alert message or warning signal may be generated to havethe plant measurements inspected and rectified. Based on the state ofhealth of the process model, new plant operating parameters may begenerated to optimize the performance of the specific plant equipment.

Similarly, the feed estimation unit 30 may receive the process modelinformation, the current plant process data, and/or previous plantlaboratory data associated with the specific plant equipment that isreliable for feed estimation analysis. The feed estimation unit 30 mayperform evaluation of the calculated offsets based on the plantperformance fit parameters for determining the state of health of theprocess model.

For example, the state of health of the process model may be determinedbased on a difference of two offsets calculated at two different times.When the difference is greater than a predetermined threshold, anotheralert message or warning signal may be generated. Based on the state ofhealth of the process model, new plant operating parameters may begenerated to optimize the performance of the specific plant equipment.

In some embodiments, the feed estimation unit 30 may infer the feedcomposition based on the product composition without substantiallyrelying on the previous plant laboratory data. In some embodiments, atleast one environmental factor, such as a temperature or pressure level,may be evaluated to determine the reliability of the productcomposition. When the product composition is determined to be reliable,the feed composition may be estimated or corrected based on the productcomposition associated with the corresponding plant equipment. Forexample, a component or ingredient analysis of the product compositionmay be performed to infer a corresponding ingredient ratio in the feedcomposition. Conversely, the product composition may be inferred basedon the component or ingredient analysis of the feed composition in areverse order.

Referring now to FIG. 4, a simplified flow diagram is depicted of anillustrative method of improving operation of a plant, such as the oneor more plants 12 a-12 n of FIGS. 1 and 2, according to some embodimentsof this disclosure. Although the following steps are primarily describedwith respect to the embodiments of FIGS. 1 and 2, the steps within themethod may be modified and executed in a different order or sequencewithout altering the principles of the present disclosure. Additionally,in some embodiments, some steps may be performed more than once, whilesome steps might not be performed at all.

The method begins at step 100. In step 102, the data cleansing system 10may be initiated by a computer system that may be local to or remotefrom the one or more plants 12 a-12 n. The method may be automaticallyor manually performed by the computer system. For example, one or moresteps may include manual operations or data inputs from the sensors andother related systems.

In step 104, the data cleansing system 10 may obtain plant operationinformation or plant data from the one or more plants 12 a-12 n over thenetwork 16. The desirable plant operation information or plant data mayinclude plant operational parameters, plant process condition data orplant process data, plant lab data, and/or information about plantconstraints. As used herein, “plant lab data” refers to the results ofperiodic laboratory analyses of fluids taken from an operating processplant. As used herein, “plant process data” refers to data measured bysensors in the process plant.

In step 106, a plant process model may be generated using the plantoperation information. The plant process model may estimate or predictplant performance based upon the plant (e.g., one or more plants 12 a-12n) operation information. The plant process model results may be used tomonitor the health of the one or more plants 12 a-12 n and/or todetermine whether any upset or poor measurement occurred. The plantprocess model may be desirably generated by an iterative process thatmodels at various plant constraints to determine the desired plantprocess model.

In step 108, a process simulation unit may be utilized to model theoperation of the one or more plants 12 a-12 n. Because the simulationfor the entire unit may, in some instances, be large and complex tosolve in a reasonable amount of time, the one or more plants 12 a-12 nmay be divided into smaller virtual sub-sections consisting of relatedunit operations. An illustrative process simulation unit, such as aUniSim® Design Suite, is disclosed in U.S. Patent Publication No.2010/0262900, now U.S. Pat. No. 9,053,260, which is incorporated byreference in its entirety. Other illustrative related systems aredisclosed in commonly assigned U.S. patent Application Ser. Nos.15/084,237 and 15/084,291 (Attorney Docket Nos. H0049260-01-8500 andH0049323-01-8500, both filed on Mar. 29, 2016), which are incorporatedby reference in their entirety.

For example, in some embodiments, a fractionation column and its relatedequipment such as its condenser, receiver, reboiler, feed exchangers,and pumps may make up a sub-section. Some or all available plant datafrom the unit, including temperatures, pressures, flows, and/orlaboratory data may be included in the simulation as Distributed ControlSystem (DCS) variables. Multiple sets of the plant data may be comparedagainst the process model for use in calculating model fittingparameters and measurement offsets that generate the smallest errors.

In step 110, the age of the plant lab data may be evaluated againstuser-defined age criteria. For example, in some embodiments, the plantlab data may be considered to be current if the sample was taken withina threshold amount of time (e.g., one hour, two hours, three hours, fourhours, five hours, six hours, seven hours, eight hours, 12 hours, 24hours, 48 hours, one week) of the current plant process data. If theplant lab data is current, control proceeds to step 114. Otherwise,control proceeds to step 112.

In step 112, when the age of the plant lab data is not current, theplant process data and model calculations may be used to estimate theplant laboratory data that is not current. For example, if thetemperature and pressure associated with the product composition areconsistent and reliable for a predetermined period, the feed compositionmay be estimated or corrected based on the last known productcomposition and/or the current plant process data.

In some embodiments, an offset may be calculated as the differencebetween plant temperature measurement and the calculated correspondingtemperature in the model; as the difference between plant pressuremeasurement and the calculated corresponding pressure in the model; oras the difference between plant flow measurement and the calculatedcorresponding flow in the model. Offsets may be calculated for one ormore of the plant measurements. In some embodiments, this may beaccomplished using an SQP (“Sequential Quadratic Programming”) optimizerthat may be configured to minimize the sum of the squares of theoffsets. In some embodiments, the SQP optimizer that is included inUniSim® Design Suite may be used.

In step 114, offsets and model parameters may be adjusted to provide thebest fit between the plant process data and the corresponding modelvalues, and the plant lab data and the corresponding model values.Offsets may be calculated as the differences between the plant processdata and plant lab data and the corresponding model variables. Modelparameters may be variables in the model that control interactionsbetween the model values that correspond to plant process data or plantlab data.

In some embodiments, an offset may be calculated as the differencebetween plant temperature measurement and the calculated correspondingtemperature in the model; as the difference between plant pressuremeasurement and the calculated corresponding pressure in the model; asthe difference between plant flow measurement and the calculatedcorresponding flow in the model; or as the difference between plantlaboratory measurement and the calculated corresponding composition inthe model. Offsets may be calculated for one or more of the plantmeasurements.

In some embodiments, model parameters may be variables within a processmodel that govern how measurements interact. As an example, a modelparameter may refer to the tray efficiency in a fractionation column, afouling factor in a heat exchanger, or a reaction rate kinetic parameterin a reactor.

Model parameters and offsets may be chosen such that the offsets betweenthe measured values and the corresponding model values are minimized. Insome embodiments, this may be accomplished using an SQP optimizerconfigured to minimize the sum of the squares of the offsets. In someembodiments, the SQP optimizer that is included in UniSim® Design Suitemay be used.

In step 116, the calculated offsets measured between the feed andproduct information may be evaluated based on evaluation criteria, whichmay be based on the expected variability of the measurement. In someembodiments, the criteria may be the expected repeatability of themeasurement sensor. In some embodiments, the criteria may be ahistorical statistical repeatability of the measurement, for example, amultiple of the standard deviation of the measurement.

In step 118, when the offset is less than or equal to a predeterminedvalue, control returns to step 104. When the offset is greater than thepredetermined value, control proceeds to step 120. Individualmeasurements with large errors may be eliminated from the fittingalgorithm. An alert message or warning signal may be raised to have themeasurement inspected and rectified.

In step 120, the operational status of plant equipment may be diagnosedbased on the at least one environmental factor and/or the calculatedoffset. As discussed above, the calculated offset between the feed andproduct information may be evaluated based on the at least oneenvironmental factor for detecting the fault of specific equipment. Itis advantageous that at least one piece of plant equipment may beevaluated and diagnosed for the fault without distributing measurementerrors for the rest of plant equipment. As an example, the single feedflow meter and/or one of two product flow meters may be diagnosed basedon their temperatures, pressure levels, and/or chemical compositions ofeach corresponding stream. The method ends at step 122.

SPECIFIC EMBODIMENTS

While the following is described in conjunction with specificembodiments, this description is intended to illustrate and not limitthe scope of the preceding description and the appended claims.

A first embodiment of the disclosure may include a system for improvingoperation of a plant, the cleansing system including a server coupled tothe cleansing system for communicating with the plant via acommunication network; a computer system having a web-based platform forreceiving and sending plant data related to the operation of the plantover the network; a display device for interactively displaying theplant data; a data cleansing unit configured for performing an enhanceddata cleansing process for allowing an early detection and diagnosis ofthe operation of the plant based on at least one environmental factor,wherein the data cleansing unit calculates and evaluates an offsetamount representing a difference between feed and product informationfor detecting an error of equipment during the operation of the plantbased on the plant data; and/or a feed estimation unit configured forestimating a feed composition associated with the equipment of the plantbased on the calculated offset amount between the feed and productinformation, wherein the feed estimation unit evaluates the calculatedoffset amount based on the at least one environmental factor fordetecting the error of the equipment during the operation of the plant.An embodiment of the disclosure is one, any or all of prior embodimentsin this paragraph up through the first embodiment in this paragraph,wherein the feed estimation unit is configured to establish a last knownfeed composition as a base point, and to modify the last known feedcomposition for providing more accurate composition data based on thecalculated offset amount. An embodiment of the disclosure is one, any orall of prior embodiments in this paragraph up through the firstembodiment in this paragraph, wherein the data cleansing unit isconfigured to receive at least one set of actual measured data from theplant on a recurring basis at a predetermined time interval. Anembodiment of the disclosure is one, any or all of prior embodiments inthis paragraph up through the first embodiment in this paragraph,wherein the data cleansing unit is configured to analyze the receiveddata for completeness and correct an error in the received data for ameasurement issue and an overall mass balance closure to generate a setof reconciled plant data. An embodiment of the disclosure is one, any orall of prior embodiments in this paragraph up through the firstembodiment in this paragraph, wherein the data cleansing unit isconfigured such that the corrected data is used as an input to asimulation process in which a process model is tuned to ensure that thesimulation process matches the reconciled plant data. An embodiment ofthe disclosure is one, any or all of prior embodiments in this paragraphup through the first embodiment in this paragraph, wherein the datacleansing unit is configured such that an output of the reconciled plantdata is input into a tuned flowsheet, and is generated as a predicteddata. An embodiment of the disclosure is one, any or all of priorembodiments in this paragraph up through the first embodiment in thisparagraph, wherein the data cleansing unit is configured such that adelta value representing a difference between the reconciled plant dataand the predicted data is validated to ensure that a viable optimizationcase is established for a simulation process run. An embodiment of thedisclosure is one, any or all of prior embodiments in this paragraph upthrough the first embodiment in this paragraph, further including areconciliation unit configured for reconciling actual measured data fromthe plant in comparison with a performance process model result from asimulation engine based on a set of predetermined reference or setpoints. An embodiment of the disclosure is one, any or all of priorembodiments in this paragraph up through the first embodiment in thisparagraph, wherein the reconciliation unit is configured to perform aheuristic analysis against the actual measured data and the performanceprocess model result using a set of predetermined threshold values, andwherein the reconciliation unit is configured to receive the plant datafrom the plant via the computer system, and the received plant datarepresent the actual measured data from the equipment in the plantduring a predetermined time period. An embodiment of the disclosure isone, any or all of prior embodiments in this paragraph up through thefirst embodiment in this paragraph, further including a diagnosis unitconfigured for diagnosing an operational status of the equipment bycalculating the offset amount based on the at least one environmentalfactor without distributing a measurement error for the rest of theequipment for the plant. An embodiment of the disclosure is one, any orall of prior embodiments in this paragraph up through the firstembodiment in this paragraph, wherein the diagnosis unit is configuredto receive the feed and product information from the plant to evaluatethe equipment, and to determine a target tolerance level of a finalproduct based on at least one of an actual current operational parameterand a historical operational parameter for detecting the error of theequipment based on the target tolerance level. An embodiment of thedisclosure is one, any or all of prior embodiments in this paragraph upthrough the first embodiment in this paragraph, wherein the datacleansing unit receives process model information relating to at leastone of a current process model of a simulation engine, current plantprocess data associated with the equipment of the plant, and currentplant laboratory data associated with the equipment of the plant. Anembodiment of the disclosure is one, any or all of prior embodiments inthis paragraph up through the first embodiment in this paragraph,wherein the data cleansing unit is configured to transmit the calculatedoffset and at least one plant performance fit parameter to the feedestimation unit for evaluation. An embodiment of the disclosure is one,any or all of prior embodiments in this paragraph up through the firstembodiment in this paragraph, wherein the data cleansing unit isconfigured to perform a tuning of a process model of a simulationengine, and determine a state of health of the process model based on atuning result, and wherein a new plant operating parameter is generatedbased on the state of health of the process model to optimize aperformance of the equipment of the plant. An embodiment of thedisclosure is one, any or all of prior embodiments in this paragraph upthrough the first embodiment in this paragraph, wherein the feedestimation unit is configured to perform a feed estimation analysis forinferring the feed composition based on a product composition associatedwith the equipment of the plant.

A second embodiment of the disclosure is a method for improvingoperation of a plant, the cleansing method including providing a servercoupled to a cleansing system for communicating with the plant via acommunication network; providing a computer system having a web-basedplatform for receiving and sending plant data related to the operationof the plant over the network; providing a display device forinteractively displaying the plant data, the display device beingconfigured for graphically or textually receiving the plant data;obtaining the plant data from the plant over the network; performing anenhanced data cleansing process for allowing an early detection anddiagnosis of the operation of the plant based on at least oneenvironmental factor; calculating and evaluating an offset amountrepresenting a difference between feed and product information fordetecting an error of equipment during the operation of the plant basedon the plant data; estimating a feed composition associated with theequipment of the plant based on the calculated offset amount between thefeed and product information; and evaluating the calculated offsetamount based on the at least one environmental factor for detecting theerror of the equipment during the operation of the plant. An embodimentof the disclosure is one, any or all of prior embodiments in thisparagraph up through the second embodiment in this paragraph, furtherincluding evaluating the at least one environmental factor for apredetermined period to determine a reliability of a product compositionassociated with the equipment of the plant. An embodiment of thedisclosure is one, any or all of prior embodiments in this paragraph upthrough the second embodiment in this paragraph, further includingevaluating the feed and product information of the equipment fordetecting the error of the equipment based on a corresponding offsetbetween the feed and product information. An embodiment of thedisclosure is one, any or all of prior embodiments in this paragraph upthrough the second embodiment in this paragraph, further includingperforming a feed estimation analysis for inferring the feed compositionbased on a product composition associated with the equipment of theplant. An embodiment of the disclosure is one, any or all of priorembodiments in this paragraph up through the second embodiment in thisparagraph, further including diagnosing an operational status of theequipment by calculating the offset amount based on the at least oneenvironmental factor without distributing a measurement error for therest of the equipment for the plant.

Without further elaboration, by using the preceding description, oneskilled in the art may utilize the present disclosure to its fullestextent and easily ascertain the essential characteristics of thisdisclosure, without departing from the spirit and scope thereof, to makevarious changes and modifications of the disclosure and to adapt it tovarious usages and conditions. The preceding specific embodiments are,therefore, to be construed as merely illustrative, and not limiting theremainder of the disclosure in any way whatsoever, and that thedisclosure is intended to cover various modifications and equivalentarrangements included within the scope of the appended claims.

In the foregoing, all temperatures are set forth in degrees Celsius and,all parts and percentages are by weight, unless otherwise indicated.While a particular embodiment of the present data cleansing system hasbeen described herein, it will be appreciated by those skilled in theart that changes and modifications may be made thereto without departingfrom the disclosure in its broader aspects and as set forth in thefollowing claims.

What is claimed is:
 1. A cleansing system for a petrochemical plant, thecleansing system comprising: a fractionation column; a condenser; apump; one or more sensors configured to collect operation data relatedto operation of the petrochemical plant, the operation data collectedfrom at least one of the fractionation column, the condenser, or thepump, the operation data comprising at least one environmental factor,feed information, and product information; an interface platformconfigured for receiving from and sending to the one or more sensors,over a network, the operation data; a cleansing platform comprising: atleast one first processor; and first non-transitory computer-readablememory storing executable instructions that, when executed by the atleast one first processor, cause the cleansing platform to: receive, viathe interface platform and from the one or more sensors, the at leastone environmental factor; and calculate an offset amount representing adifference between the feed information and the product information; anda feed estimation platform comprising: at least one second processor;and second non-transitory computer-readable memory storing executableinstructions that, when executed by the at least one second processor,cause the feed estimation platform to: receive the offset amountrepresenting the difference between the feed information and the productinformation; receive the at least one environmental factor; evaluate theoffset amount using the at least one environmental factor; detect, basedon evaluating the offset amount, an equipment error during operation ofthe petrochemical plant; and transmit an alert of the equipment error toa display device associated with the petrochemical plant.
 2. Thecleansing system of claim 1, wherein the second non-transitorycomputer-readable memory of the feed estimation platform stores furtherexecutable instructions that, when executed by the at least one secondprocessor, cause the feed estimation platform to: establish a last knownfeed composition as a base point; and modify the last known feedcomposition based on the calculated offset amount.
 3. The cleansingsystem of claim 1, wherein the first non-transitory computer-readablememory of the cleansing platform stores further executable instructionsthat, when executed by the at least one first processor, cause thecleansing platform to: receive a set of actual measured data from theone or more sensors of the petrochemical plant on a recurring basis at apredetermined time interval.
 4. The cleansing system of claim 3, whereinthe first non-transitory computer-readable memory of the cleansingplatform stores further executable instructions that, when executed bythe at least one first processor, cause the cleansing platform to:analyze the received set of actual measured data for completeness;correct a first error in the received set of actual measured data for ameasurement issue; correct a second error in the received set of actualmeasured data for an overall mass balance closure; and generate, usingthe corrected received set of actual measured data, a set of reconciledplant data.
 5. The cleansing system of claim 4, wherein the firstnon-transitory computer-readable memory of the cleansing platform storesfurther executable instructions that, when executed by the at least onefirst processor, cause the cleansing platform to: use the correctedreceived set of actual measured data as an input to a simulation processin which a process model is tuned to ensure that the simulation processmatches the set of reconciled plant data.
 6. The cleansing system ofclaim 4, wherein the first non-transitory computer-readable memory ofthe cleansing platform stores further executable instructions that, whenexecuted by the at least one first processor, cause the cleansingplatform to: input an output of the set of reconciled plant data into atuned flowsheet; and generate predicted data from the output of the setof reconciled plant data.
 7. The cleansing system of claim 6, whereinthe first non-transitory computer-readable memory of the cleansingplatform stores further executable instructions that, when executed bythe at least one first processor, cause the cleansing platform to:validate a delta value representing a difference between the set ofreconciled plant data and the predicted data to establish a viableoptimization case for a simulation process run.
 8. The cleansing systemof claim 1, further comprising: a reconciliation platform comprising: atleast one third processor; and third non-transitory computer-readablememory storing executable instructions that, when executed by the atleast one third processor, cause the reconciliation platform to: receivea set of actual measured data from the one or more sensors of thepetrochemical plant; and reconcile the set of actual measured data fromthe petrochemical plant with a performance process model result from asimulation engine based on a set of predetermined reference or setpoints.
 9. The cleansing system of claim 8, wherein the thirdnon-transitory computer-readable memory of the reconciliation platformstores further executable instructions that, when executed by the atleast one first processor, cause the reconciliation platform to: performa heuristic analysis against the set of actual measured data and theperformance process model result using a set of predetermined thresholdvalues; and receive the operation data from the petrochemical plant viathe interface platform, wherein the received operation data representsthe set of actual measured data from the petrochemical plant during apredetermined time period.
 10. The cleansing system of claim 1, furthercomprising: a diagnosis platform comprising: at least one thirdprocessor; and third non-transitory computer-readable memory storingexecutable instructions that, when executed by the at least one thirdprocessor, cause the diagnosis platform to: diagnose an operationalstatus of first equipment of the petrochemical plant by calculating theoffset amount based on the at least one environmental factor withoutdistributing a measurement error for a plurality of different equipmentof the petrochemical plant.
 11. The cleansing system of claim 10,wherein the third non-transitory computer-readable memory of thediagnosis platform stores further executable instructions that, whenexecuted by the at least one third processor, cause the diagnosisplatform to: receive the feed information and the product informationfrom the petrochemical plant; and determine a target tolerance level ofa final product based on at least one of an actual current operationalparameter or a historical operational parameter, wherein the targettolerance level is used for detecting the equipment error.
 12. Thecleansing system of claim 1, wherein the first non-transitorycomputer-readable memory of the cleansing platform stores furtherexecutable instructions that, when executed by the at least one firstprocessor, cause the cleansing platform to: receive process modelinformation relating to at least one of: a current process model of asimulation engine, current plant process data associated with thepetrochemical plant, or current plant laboratory data associated withthe petrochemical plant.
 13. The cleansing system of claim 1, whereinthe first non-transitory computer-readable memory of the cleansingplatform stores further executable instructions that, when executed bythe at least one first processor, cause the cleansing platform to:transmit the calculated offset and at least one plant performance fitparameter to the feed estimation platform for evaluation.
 14. Thecleansing system of claim 1, wherein the first non-transitorycomputer-readable memory of the cleansing platform stores furtherexecutable instructions that, when executed by the at least one firstprocessor, cause the cleansing platform to: tune a process model of asimulation engine to determine a tuning result; determine a state ofhealth of the process model based on the tuning result; and generate anew plant operating parameter based on the state of health of theprocess model to optimize a performance of the petrochemical plant. 15.The cleansing system of claim 1, wherein the second non-transitorycomputer-readable memory of the feed estimation platform stores furtherexecutable instructions that, when executed by the at least one secondprocessor, cause the feed estimation platform to: perform a feedestimation analysis; and use the feed estimation analysis to infer afeed composition based on a product composition associated with thepetrochemical plant.
 16. A cleansing method for a petrochemical plantcomprising a fractionation column, a condenser, and a pump, thecleansing method comprising: receiving, by a computing device and fromone or more sensors associated with at least one of the fractionationcolumn, the condenser, or the pump, operation data related to operationof the petrochemical plant, the operation data comprising at least oneenvironmental factor, feed information, and product information;receiving, by the computing device and from the one or more sensors, theat least one environmental factor; calculating, by the computing device,an offset amount representing a difference between the feed informationand the product information; evaluating, by the computing device, theoffset amount using the at least one environmental factor; estimating,by the computing device, a feed composition associated with thepetrochemical plant based on the offset amount between the feedinformation and the product information; detecting, by the computingdevice, based on the offset amount, an equipment error during operationof the petrochemical plant; and transmitting, by the computing device,an alert of the equipment error to a display device associated with thepetrochemical plant.
 17. The cleansing method of claim 16, comprising:evaluating the at least one environmental factor to determine areliability over a period of time of a product composition associatedwith the petrochemical plant.
 18. The cleansing method of claim 16,wherein evaluating, by the computing device, the offset amount comprisesevaluating the offset amount using the feed information and the productinformation, and wherein detecting the equipment error is based on theoffset amount representing the difference between the feed informationand the product information.
 19. The cleansing method of claim 16,comprising: performing a feed estimation analysis; and using the feedestimation analysis, inferring the feed composition based on a productcomposition associated with the petrochemical plant.
 20. The cleansingmethod of claim 16, comprising: diagnosing an operational status offirst equipment of the petrochemical plant by calculating the offsetamount based on the at least one environmental factor withoutdistributing a measurement error for a plurality of different equipmentof the petrochemical plant.